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Record W577586447 · doi:10.1016/j.dib.2015.05.019

Quantitative analysis of the myelin g -ratio from electron microscopy images of the macaque corpus callosum

2015· article· en· W577586447 on OpenAlexafffund
Nikola Stikov, Jennifer S. W. Campbell, Thomas Stroh, Mariette Lavelée, Stephen Frey, Jennifer Novek, Stephen G. Nuara, Ming‐Kai Ho, Barry J. Bedell, Robert F. Dougherty, Ilana R. Leppert, Mathieu Boudreau, Sridar Narayanan, Tanguy Duval, Julien Cohen‐Adad, Paul-Alexandre Picard, Alicja Gąsecka, Daniel Côté, G. Bruce Pike

Bibliographic record

VenueData in Brief · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of CalgaryUniversité LavalMontreal Neurological Institute and HospitalMcGill UniversityPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMyelinCorpus callosumAxonMacaqueMagnetic resonance imagingElectron microscopeStereologyAnatomyMyelin sheathAspect ratio (aeronautics)BiologyPathologyChemistryNuclear magnetic resonanceMaterials scienceMedicinePhysicsNeuroscienceCentral nervous systemOpticsRadiology

Abstract

fetched live from OpenAlex

We provide a detailed morphometric analysis of eight transmission electron micrographs (TEMs) obtained from the corpus callosum of one cynomolgus macaque. The raw TEM images are included in the article, along with the distributions of the axon caliber and the myelin g-ratio in each image. The distributions are analyzed to determine the relationship between axon caliber and g-ratio, and compared against the aggregate metrics (myelin volume fraction, fiber volume fraction, and the aggregate g-ratio), as defined in the accompanying research article entitled 'In vivo histology of the myelin g-ratio with magnetic resonance imaging' (Stikov et al., NeuroImage, 2015).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.419
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2015
Admission routes2
Has abstractyes

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